An efficient variation of bubble sort

Dobosiewicz, W.

doi:10.1016/0020-0190(80)90022-8

Cited by 28 publications

(12 citation statements)

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“…The gg_sort routine included in SPEC CPU2006 component gobmk [18] implements the combsort algorithm [24], a variant of bubblesort that compares items at distance larger than 1. Combsort is known to have at least quadratic running time in the worst case, but can rival fast algorithms like quicksort in many practical cases.…”

Section: Spec Benchmarksmentioning

confidence: 99%

Input-Sensitive Profiling

Coppa

Demetrescu

Finocchi

2014

IIEEE Trans. Software Eng.

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In this article we present a building block technique and a toolkit towards automatic discovery of workload-dependent performance bottlenecks. From one or more runs of a program, our profiler automatically measures how the performance of individual routines scales as a function of the input size, yielding clues to their growth rate. The output of the profiler is, for each executed routine of the program, a set of tuples that aggregate performance costs by input size. The collected profiles can be used to produce performance plots and derive trend functions by statistical curve fitting techniques. A key feature of our method is the ability to automatically measure the size of the input given to a generic code fragment: to this aim, we propose an effective metric for estimating the input size of a routine and show how to compute it efficiently. We discuss several examples, showing that our approach can reveal asymptotic bottlenecks that other profilers may fail to detect and can provide useful characterizations of the workload and behavior of individual routines in the context of mainstream applications, yielding several code optimizations as well as algorithmic improvements. To prove the feasibility of our techniques, we implemented a Valgrind tool called aprof and performed an extensive experimental evaluation on the SPEC CPU2006 benchmarks. Our experiments show that aprof delivers comparable performance to other prominent Valgrind tools, and can generate informative plots even from single runs on typical workloads for most algorithmically-critical routines

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Section: Spec Benchmarksmentioning

confidence: 99%

Input-Sensitive Profiling

Coppa

Demetrescu

Finocchi

2014

IIEEE Trans. Software Eng.

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“…Then we could define K , called ordered factor. n K N = (2) [ ] 0,1 K ∈ reflects the sort degree of a random sequence. K is bigger, more ordered exists in the sequence.…”

Section: B Performances Evaluationmentioning

confidence: 99%

Experimental study on the five sort algorithms

You

Gan

2011

2011 Second International Conference on Mechanic Automation and Control Engineering

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Sorting algorithm is one of the most basic research fields in computer science. It's goal is to make record easier to search, insert and delete. Through the description of five sort algorithms: bubble, select, insert, merger and quick, the time and space complexity was summarized. Furthermore, two categories of ( )O n n could be found out. From the aspects of input sequence scale and input sequence random degree, some results were obtained based on the experiments. When the size of records is small, insertion sort or selection sort performs well. When the sequence is ordered, insertion sort or bubble sort performs well. When the size of records is large, quick sort or merge sort performs well. Different application could select appropriate sort algorithm according to these rules.

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“…This variant was rediscovered later by Dobosiewicz [2] and Box and Lacey [1]. Box and Lacey [1] named this variant comb sort.…”

Section: Shellsort Variantsmentioning

confidence: 99%

“…Box and Lacey [1] named this variant comb sort. The increment sequence used by comb sort is geometric with factor s. Dobosiewicz [2] has shown that the preprocessing step sorts a[0 : n− 1] with very high probability whenever s < 1.33. As a result, s = 1.3 is recommended in practice (note that a larger s requires a smaller number of h-passes).…”

Section: Shellsort Variantsmentioning

confidence: 99%

Sorting on a Cell Broadband Engine SPU

Bandyopadhyay

Sahni

2009

2009 IEEE Symposium on Computers and Communications

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We adapt merge sort for a single SPU of the Cell Broadband Engine. This adaptation takes advantage of the vector instructions supported by the SPU. Experimental results indicate that our merge sort adaptation is faster than other sort algorithms (e.g., AA sort, Cell sort, quick sort) proposed for the SPU as well as faster than our SPU adaptations of shaker sort and brick sort. An added advantage is that our merge sort adaptation is a stable sort whereas none of the other sort adaptations is stable.

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An efficient variation of bubble sort

Cited by 28 publications

References 1 publication

Input-Sensitive Profiling

Input-Sensitive Profiling

Experimental study on the five sort algorithms

Sorting on a Cell Broadband Engine SPU

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